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# Owner(s): ["module: dynamo"]
import unittest
import torch._dynamo
from torch._dynamo.test_minifier_common import MinifierTestBase
from torch.testing._internal.common_utils import skipIfNNModuleInlined
requires_cuda = unittest.skipUnless(torch.cuda.is_available(), "requires cuda")
class MinifierTests(MinifierTestBase):
# Test that compile, runtime, and accuracy errors after dynamo can be repro'd (both CPU and CUDA)
def _test_after_dynamo(self, device, backend, expected_error):
run_code = f"""\
@torch.compile(backend={backend!r})
def inner(x):
for _ in range(10):
x = torch.sin(x)
x = torch.relu(x)
for _ in range(10):
x = torch.cos(x)
return x
inner(torch.randn(20, 20).to("{device}"))
"""
self._run_full_test(run_code, "dynamo", expected_error, isolate=False)
def test_after_dynamo_cpu_compile_error(self):
self._test_after_dynamo(
"cpu", "relu_compile_error_TESTING_ONLY", "ReluCompileError"
)
def test_after_dynamo_cpu_runtime_error(self):
self._test_after_dynamo(
"cpu", "relu_runtime_error_TESTING_ONLY", "ReluRuntimeError"
)
def test_after_dynamo_cpu_accuracy_error(self):
self._test_after_dynamo(
"cpu", "relu_accuracy_error_TESTING_ONLY", "AccuracyError"
)
@requires_cuda
def test_after_dynamo_cuda_compile_error(self):
self._test_after_dynamo(
"cuda", "relu_compile_error_TESTING_ONLY", "ReluCompileError"
)
@requires_cuda
def test_after_dynamo_cuda_runtime_error(self):
self._test_after_dynamo(
"cuda", "relu_runtime_error_TESTING_ONLY", "ReluRuntimeError"
)
@requires_cuda
def test_after_dynamo_cuda_accuracy_error(self):
self._test_after_dynamo(
"cuda", "relu_accuracy_error_TESTING_ONLY", "AccuracyError"
)
def test_after_dynamo_non_leaf_compile_error(self):
run_code = """\
@torch.compile(backend="non_leaf_compile_error_TESTING_ONLY")
def inner(x):
return x + 1
inner(torch.randn(20, 20, requires_grad=True) + 1)
"""
self._run_full_test(
run_code, "dynamo", "TestingOnlyCompileError", isolate=False
)
# Ensure that the testing backends pass when relu is not present.
def _test_after_dynamo_backend_passes(self, device, backend):
@torch.compile(backend=backend)
def inner(x):
for _ in range(10):
x = torch.sin(x)
for _ in range(10):
x = torch.cos(x)
return x
inner(torch.randn(20, 20).to(device))
def test_after_dynamo_cpu_compile_backend_passes(self):
self._test_after_dynamo_backend_passes("cpu", "relu_compile_error_TESTING_ONLY")
def test_after_dynamo_cpu_runtime_backend_passes(self):
self._test_after_dynamo_backend_passes("cpu", "relu_runtime_error_TESTING_ONLY")
def test_after_dynamo_cpu_accuracy_backend_passes(self):
self._test_after_dynamo_backend_passes(
"cpu", "relu_accuracy_error_TESTING_ONLY"
)
@requires_cuda
def test_after_dynamo_cuda_compile_backend_passes(self):
self._test_after_dynamo_backend_passes(
"cuda", "relu_compile_error_TESTING_ONLY"
)
@requires_cuda
def test_after_dynamo_cuda_runtime_backend_passes(self):
self._test_after_dynamo_backend_passes(
"cuda", "relu_runtime_error_TESTING_ONLY"
)
@requires_cuda
def test_after_dynamo_cuda_accuracy_backend_passes(self):
self._test_after_dynamo_backend_passes(
"cuda", "relu_accuracy_error_TESTING_ONLY"
)
# Test that a module with mixed cpu/cuda parts with an error after dynamo can be repro'd
@skipIfNNModuleInlined()
@requires_cuda
def test_cpu_cuda_module_after_dynamo(self):
backend_name = "relu_compile_error_TESTING_ONLY"
run_code = f"""\
class CpuCudaModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.m_x = torch.nn.Linear(20, 20).cuda()
self.m_y = torch.nn.Linear(20, 20)
self.p_x = torch.nn.Parameter(torch.randn(20, 20).cuda())
self.p_y = torch.nn.Parameter(torch.randn(20, 20))
self.b_x = torch.nn.Buffer(torch.ones(20, 20).cuda())
self.b_y = torch.nn.Buffer(torch.ones(20, 20))
def forward(self, x, y):
return self.m_x(x) + self.p_x + self.b_x, self.m_y(y) + self.p_y + self.b_y
mod = CpuCudaModule()
@torch.compile(backend={backend_name!r})
def inner(x1, y1):
x2 = torch.randn(20, 20).cuda()
y2 = torch.randn(20, 20)
x3, y3 = mod(x1 + x2, y1 + y2)
return torch.relu(x3.cpu() + y3)
inner(torch.randn(20, 20).cuda(), torch.randn(20, 20))
"""
res = self._run_full_test(run_code, "dynamo", "ReluCompileError", isolate=False)
self.assertExpectedInline(
res.minifier_module(),
"""\
class Repro(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.G__mod___m_x = Linear(in_features=20, out_features=20, bias=True).cuda()
self.G__mod___m_y = Linear(in_features=20, out_features=20, bias=True)
self.register_buffer('G__mod___b_x', torch.randn([20, 20], dtype=torch.float32).cuda())
self.register_buffer('G__mod___b_y', torch.randn([20, 20], dtype=torch.float32))
self.G__mod___p_x = torch.nn.Parameter(torch.randn([20, 20], dtype=torch.float32, device="cuda"))
self.G__mod___p_y = torch.nn.Parameter(torch.randn([20, 20], dtype=torch.float32))
def forward(self, L_x1_ : torch.Tensor, L_y1_ : torch.Tensor):
l_x1_ = L_x1_
l_y1_ = L_y1_
randn = torch.randn(20, 20)
x2 = randn.cuda(); randn = None
y2 = torch.randn(20, 20)
add = l_x1_ + x2; l_x1_ = x2 = None
add_1 = l_y1_ + y2; l_y1_ = y2 = None
g__mod___m_x = self.G__mod___m_x(add); add = None
g__mod___p_x = self.G__mod___p_x
add_2 = g__mod___m_x + g__mod___p_x; g__mod___m_x = g__mod___p_x = None
g__mod___b_x = self.G__mod___b_x
x3 = add_2 + g__mod___b_x; add_2 = g__mod___b_x = None
g__mod___m_y = self.G__mod___m_y(add_1); add_1 = None
g__mod___p_y = self.G__mod___p_y
add_4 = g__mod___m_y + g__mod___p_y; g__mod___m_y = g__mod___p_y = None
g__mod___b_y = self.G__mod___b_y
y3 = add_4 + g__mod___b_y; add_4 = g__mod___b_y = None
cpu = x3.cpu(); x3 = None
add_6 = cpu + y3; cpu = y3 = None
relu = torch.relu(add_6); add_6 = None
return (relu,)""",
)
# Test if we can actually get a minified graph
def test_if_graph_minified(self):
backend_name = "relu_compile_error_TESTING_ONLY"
run_code = f"""\
@torch.compile(backend={backend_name!r})
def inner(x):
for _ in range(20):
x = torch.sin(x)
x = torch.relu(x)
for _ in range(20):
x = torch.cos(x)
return x
inner(torch.randn(20, 20))
"""
res = self._run_full_test(run_code, "dynamo", "ReluCompileError", isolate=False)
self.assertExpectedInline(
res.repro_module(),
"""\
class Repro(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x_19):
x_20 = torch.relu(x_19); x_19 = None
return (x_20,)""",
)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
|
